Reliable prediction of hypersonic aerothermodynamics depends critically on the accuracy of thermochemical nonequilibrium kinetics models, yet their experimental validation remains expensive and diagnostically constrained. This work develops a Bayesian experimental design framework to identify experimental conditions that are maximally informative for calibrating the modified Marrone-Treanor air-chemistry model. Informativeness is quantified through expected information gain using information-theoretic utilities that incorporate prior parameter uncertainty and measurement noise. To render the required repeated utility evaluations tractable for field-valued observables, we construct reduced-order surrogates via a Karhunen-Loève expansion with Gaussian process regression of the modal coefficients. The framework is demonstrated on an adiabatic zero-dimensional reactor and on hypersonic flow over a two-dimensional cylinder, producing full and marginal utility maps over design variables and measurement locations. Synthetic Bayesian inference studies confirm that designs selected from high-utility regions yield substantially tighter posteriors than designs from low-utility regions, providing a practical pathway for targeted, resource-efficient validation of nonequilibrium kinetics in hypersonic flows.

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• High-speed flow
• Nonequilibrium & irreversible thermodynamics
• Reacting flows
• Shock waves
• Stochastic inference
• Information theory
• Stochastic analysis methods